The Short-Term Load Forecasting Using an Artificial Neural Network Approach with Periodic and Nonperiodic Factors: A Case Study of Tai'an, Shandong Province, China | Zendy

Jiuyun Sun | Zendy; Huanhe Dong | Zendy; Ya Gao | Zendy; Yong Fang | Zendy; Yuan Kong | Zendy

Open Access

The Short-Term Load Forecasting Using an Artificial Neural Network Approach with Periodic and Nonperiodic Factors: A Case Study of Tai'an, Shandong Province, China

Author(s) -

Jiuyun Sun,

Huanhe Dong,

Ya Gao,

Yong Fang,

Yuan Kong

Publication year - 2021

Publication title -

computational intelligence and neuroscience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.605

H-Index - 52

eISSN - 1687-5273

pISSN - 1687-5265

DOI - 10.1155/2021/1502932

Subject(s) - term (time) , artificial neural network , electricity , china , computer science , set (abstract data type) , long term prediction , environmental science , meteorology , econometrics , artificial intelligence , geography , mathematics , telecommunications , engineering , physics , archaeology , quantum mechanics , electrical engineering , programming language

Accurate electricity load forecasting is an important prerequisite for stable electricity system operation. In this paper, it is found that daily and weekly variations are prominent by the power spectrum analysis of the historical loads collected hourly in Tai'an, Shandong Province, China. In addition, the influence of the extraneous variables is also very obvious. For example, the load dropped significantly for a long period of time during the Chinese Lunar Spring Festival. Therefore, an artificial neural network model is constructed with six periodic and three nonperiodic factors. The load from January 2016 to August 2018 was divided into two parts in the ratio of 9 : 1 as the training set and the test set, respectively. The experimental results indicate that the daily prediction model with selected factors can achieve higher forecasting accuracy.

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